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             学护理的MM求助
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               2006年9月25日 上午3:22
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               大家好, 我现在在读护理的研究生,在写毕业论文中遇到若干统计学的问题,在此请教高人指点一二.(情恕我冒昧,在此之前,我问过我们学校的公共卫生学院的统计研究生,但是她们解决不了我的问题. 若是问老师呢,呵呵,那得要MONEY.)
               <br/>
               首先,我想问名义变量如何做相关,比如,我的调查表中有婚姻状况1)未婚,2)已婚 3)离异或分居,要和工作满意度做相关.输入数据时,我就是按照1,2,3代替未婚,已婚等这样输入的.工作满意度是个量化了的分数,是呈正态分布的.我用的是spearman's Rho.结果得出一个相关系数．我想请问这个相关系数做出来有没有意义呢？比如，做出来是正相关，能对婚姻状况和工作满意度的关系怎么解释呢？似乎说不通．不能说婚姻状况怎么样，然后导致工作满意度怎么样吧．哎呀，头痛．
               <br/>
               其次，还是关于婚姻状况这样的名义变量的问题．如果做多元线形回归（stepwise)，婚姻状况也是其中一个自变量，工作满意度是因变量．像婚姻状况这样的名义变量是否要进行变量变换，比如要变成哑变量，才能引入回归方程？
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               2006年9月25日 下午2:42
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               今天我在看一本关于生存分析的书，书中有一个例子说：“研究者的兴趣在于比较三种饮食在保持老鼠不患肿瘤方面的能力。。。”，我不知道你的兴趣是什么？如果你的兴趣在于婚姻状况对工作满意度的影响，我个人认为可以把数据分成三堆来做要好些，可以做一个检验，看看三种状况下的差异是否显著；如果还有其它的变量就要考虑多因素回归，当然要把名义变量转换一下，应该是百分比。（难道您的导师不能给你点帮助吗？为什么要钱啊？）
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               2006年9月25日 下午3:11
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               你那样做的相关系数是没有意义的。做三组间的比较吧。用回归分析是可以做的，应该转换成哑变量，其结果和三组间的方差分析是一致的（如果满意度服从正态分布的话）。
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               2006年9月25日 下午3:15
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               对于离散的数据，通常的处理方法无非就是这么几种：
               <br/>
               1、列联表（Contingency table）
               <br/>
               2、方差分析（ANOVA）
               <br/>
               3、回归：Logistic Regression（因变量离散）；Dummy Variable（自变量离散）
               <br/>
               4、直观的方法：作图（散点图等）
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               应该没说全，懒得想了：）
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               2006年9月25日 下午3:18
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               呵呵,不是的.因为我的导师是美国人,你知道的,她说她是护理学的专家,不是统计学的专家,她不懂统计的问题.所以,我只能自力更生啦.我的问题就是研究人口统计学特征,压力量表,应对量表跟工作满意度之间的关系.婚姻状况就是属于人口统计学特征的一条.另外,我问过统计的研究生,她们都没用过什么哑变量.然后问她们的老师,似乎是要钱才能解答.哎.
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               2006年9月25日 下午3:28
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               谢谢斑竹,可能我的研究问题没说清楚.我做多元线形回归的目的是,要找出对工作满意度最大的影响因素.我用的是STEPWISE.对于想婚姻状况这样的名义变量,怎么输入数据到SPSS里?最后的结果是要看哪个自变量是工作满意度的predictor.请指教．比如自变量有　文化程度，婚姻状况等等．
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               2006年9月25日 下午3:39
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               还有一个问题想要请教．不能做相关的变量能做回归吗？
               <br/>
               不是正态分布的变量能不能用均数来描述？
               <br/>
               多项应答的变量，我不知道该怎样输入数据．比如：治疗方式有以下选项：１.化疗　２.放疗　３.抗雌激素治疗
               <br/>
               如果定义三个变量为x1,x2,x3．样本选择了前两项，该怎样输入？（以数字表示）
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               2006年9月25日 下午7:29
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              <p>
               I have one more comment: the normality showed in marginal distribution is almost useless in a linear model (including ANOVA, multiple regression, …). The assumption of normality is made on the "errors". Hence, checking normality should almost always be done on properly standardized the residuals (e.g. externally studentized residuals).
              </p>
              <p>
               Correlation can be mathematically well defined, but useless for practical interpretation in this case, because if you code the data as 3,2,1 instead of 1,2,3, you will completely reverse the sign of the correlation.
              </p>
              <p>
               不能做相关的变量能做回归吗？
               <br/>
               No problem. In regression, all the regressors are considered fixed or conditioned on. So they are not treated as random variables, not to say correlation, where correlation is always defined for random variables. Some confusion might show up in that the test for beta=0 is an equivalent to testing correlation=0, but this is only a special case. For anything other than zero, this is not true. In short, regression and correlation are not the same thing.
              </p>
              <p>
               不是正态分布的变量能不能用均数来描述？
               <br/>
               For the normal random variables, sample mean and sample variance (or any other two sample moments) are complete sufficient. That is to say, even for normal random variables, mean is not enough to describe the data. This is my answer to your "literal question".
               <br/>
               I guess the de facto question you are trying to ask it whether sample mean is a proper statistic to describe the central tendency for some non-normal distribution. My answer is both yes and no. Yes, because for any distribution with finite expectation, the sample mean is indeed a measure of central tendency. No, because 1) for distributions with infinite expectation (e.g. Cauchy distribution) sample mean is completely non-sense; and 2) for data with outliers (often seen in, but not restrict to, highly skewed distributions), even though sample mean is still a measure of central tendency, it is not robust enough, and hence usually not considered as the best measure of central tendency. Robust statistics like median and other forms of M-estimates than the mean are more appropriate. But the resulting analysis procedures will also be more complicated (not the usual ANOVA or multiple regression any more).
              </p>
              <p>
               我做多元线形回归的目的是,要找出对工作满意度最大的影响因素.我用的是STEPWISE.
               <br/>
               This is a common mistake. Stepwise regression can't do what you believed it can do. The causal relationship could rarely be inferred from such observational studies. Stepwise regression can only provide very weak hints to this end. So, you can still do that if you want, but be very careful to write the conclusion part in the thesis. In any case, the stepwise regression based on significance should be avoided, which would mess up the proper null distrbution (snooping data). Other criteria(AIC,BIC,…) might be better choices, but I don't know any one of them that can completely solve the problem.
              </p>
              <p>
               她说她是护理学的专家,不是统计学的专家,她不懂统计的问题.
               <br/>
               Her answer is reasonable. But I think most US universities offer free statistical consulting services for their faculties and grad students. You could try to find them to ask for help.
              </p>
              <p>
               我问过统计的研究生,她们都没用过什么哑变量.
               <br/>
               I'm guess they are not statistics students. They, at most, are health statistics or epidemiology students, who usually only received some statistical method education, without or with minimal mathematical statistics education. So, one problem for such education systems might be that their students usually can't solve unseen problems properly, even though they can sovle a lot of common problems they have seen in the textbooks.
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               2006年9月26日 上午1:36
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              <p>
               你所问的问题都是医学统计学的基本问题，最好下载一本统计学专著和SPSS书籍看下，本版有一精华贴是张文彤的SPSS高级版，里面有关于Logistic回归分析的，有关于变量的组织方式，哑变量的设置原则的内容。
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               2006年9月26日 上午2:00
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              <p>
               Thanks for your suggestion. But I am still not sure how to do analysis next.
               <br/>
               My research questions are 1) what are the relationships among chinese intensive care nurses'demographic characteristics, work stressors, coping methods and job satisfaction?
               <br/>
               2)which combination of work stressors, demographic variables and coping methods significantly predict job satisfaction in chinese intensive care nurses?
               <br/>
               Can I chose the inpendent variable freely in multiple regression? for example, remove the marital status from the demographic characteristics.
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               2006年9月26日 上午2:00
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               <p>
                [b]引用第6楼[i]xipin[/i]于[i]2006-09-25 22:39[/i]发表的“”[/b]:
                <br/>
                还有一个问题想要请教．不能做相关的变量能做回归吗？
                <br/>
                不是正态分布的变量能不能用均数来描述？
                <br/>
                多项应答的变量，我不知道该怎样输入数据．比如：治疗方式有以下选项：１.化疗　２.放疗　３.抗雌激素治疗
                <br/>
                如果定义三个变量为x1,x2,x3．样本选择了前两项，该怎样输入？（以数字表示）
               </p>
              </blockquote>
              <p>
               1.原则上说不能做相关分析（非正态分布的变量或者非随机变量）也不能做回归分析，进行回归分析的条件要壁相关分析严格的多，回归分析是要研究结果变量与指示变量间的定量化的关系，用来进行从量化的角度来研究，即结果变量随指示变量变化其数量的依从变化，变化的大小、方向等。
               <br/>
               2.  非正态分布的变量，如果经过适当的变量变换，如取对数、归一化等变换后再检验如服从正泰分布，还示可以用相应变换关系的均数表示，否则应采用中位数、众数等来标志其分布。
               <br/>
               3.多项应答的变量数据输入有两种方式：根据你的要求，有X1-X3三个选项。而答案的编码规则为：按填写的顺序值编码，治疗方式有以下选项1表示X1，2表示X2，3表示X3。解决多选问题的SPSS过程：
               <br/>
               首先将每个题的若干答案组成一个综合变量即变量集(Set)，然后对综合变量的各种取值进行分析。
               <br/>
               多选问题的分析在SPSS中是通过Analyze + Multiple Response中的各项功能实现的。
               <br/>
               Define Sets —建立多选二分变量或多选分类变量集（用一个变量代表多个变量），并应用于频数表和交叉表。
               <br/>
               Frequencies：对多选变量集进行频数分布分析
               <br/>
               Crosstabs：对多选变量集与其他变量集或与原基本变量进行交叉表分析。
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               2006年9月26日 上午2:10
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                [b]引用第0楼[i]xipin[/i]于[i]2006-09-25 17:22[/i]发表的“学护理的MM求助”[/b]:
               </p>
               <p>
                大家好, 我现在在读护理的研究生,在写毕业论文中遇到若干统计学的问题,在此请教高人指点一二.(情恕我冒昧,在此之前,我问过我们学校的公共卫生学院的统计研究生,但是她们解决不了我的问题. 若是问老师呢,呵呵,那得要MONEY.)
                <br/>
                首先,我想问名义变量如何做相关,比如,我的调查表中有婚姻状况1)未婚,2)已婚 3)离异或分居,要和工作满意度做相关.输入数据时,我就是按照1,2,3代替未婚,已婚等这样输入的.工作满意度是个量化了的分数,是呈正态分布的.我用的是spearman's Rho.结果得出一个相关系数．我想请问这个相关系数做出来有没有意义呢？比如，做出来是正相关，能对婚姻状况和工作满意度的关系怎么解释呢？似乎说不通．不能说婚姻状况怎么样，然后导致工作满意度怎么样吧．哎呀，头痛．
                <br/>
                其次，还是关于婚姻状况这样的名义变量的问题．如果做多元线形回归（stepwise)，婚姻状况也是其中一个自变量，工作满意度是因变量．像婚姻状况这样的名义变量是否要进行变量变换，比如要变成哑变量，才能引入回归方程？
               </p>
              </blockquote>
              <p>
               有两个办法解决：
               <br/>
               1.把工作满意度量化分值转换成等级性变量，如分为5个等级。做列联分析，看两者间的关系
               <br/>
               2.就是像Amzon07所说的：对自变量引入哑变量。
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               2006年9月26日 下午2:02
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                [b]引用第10楼[i]netcow[/i]于[i]2006-09-26 09:00[/i]发表的“”[/b]:
               </p>
               <p>
                1.原则上说不能做相关分析（非正态分布的变量或者非随机变量）也不能做回归分析，进行回归分析的条件要壁相关分析严格的多，回归分析是要研究结果变量与指示变量间的定量化的关系，用来进行从量化的角度来研究，即结果变量随指示变量变化其数量的依从变化，变化的大小、方向等。
               </p>
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              <p>
               I can't find any reason that "in principle" the correlation can't be calculated. Just on the contrary, "in principle" correlation is perfectly defined between a discrete RV and a continuous RV. This has nothing to do with normality, as long as both of the RVs have finite variance. Note that I'm not recommending her to do this in this particular problem.
               <br/>
               Again, regression needs no normality assumption, normality only makes the small sample exact theory possible. I'd like to know what do you mean by "要壁相关分析严格的多".
              </p>
              <blockquote class="d4pbbc-quote">
               <p>
                2.  非正态分布的变量，如果经过适当的变量变换，如取对数、归一化等变换后再检验如服从正泰分布，还示可以用相应变换关系的均数表示，否则应采用中位数、众数等来标志其分布。
                <br/>
                …….
               </p>
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              <p>
               Transformation is one possible way, but almost the last choice to consider.
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